{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 构建数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_moons(1000,random_state = 260)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.36589911, 0.93065452],\n",
       "       [0.44075878, 0.89762559],\n",
       "       [0.32454276, 0.94587102],\n",
       "       ...,\n",
       "       [1.96013545, 0.22046482],\n",
       "       [0.621378  , 0.78351093],\n",
       "       [0.99801881, 0.06291619]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        1.36589911e+00,  1.96013545e+00,  6.21378005e-01,  9.98018814e-01])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "X[:,0]"
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     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a21500bd0>"
      ]
     },
     "execution_count": 7,
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     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 添加噪声后的数据\n",
    "X,y = make_moons(10000,noise=0.5,random_state=260)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a214f2e90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基分类器：KNN、决策树、逻辑回归\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import VotingClassifier # Voting投票法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 采用三个分类器进行建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "dt = DecisionTreeClassifier()\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建投票分类器   estimators 分类器列表    #voting='hard' 硬投票\n",
    "vot = VotingClassifier(estimators=[('knn',knn),('tree',dt),('logistic',lr)],voting='hard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拆分数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.3,random_state=260)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('knn', KNeighborsClassifier()),\n",
       "                             ('tree', DecisionTreeClassifier()),\n",
       "                             ('logistic', LogisticRegression())])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vot.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[KNeighborsClassifier(), DecisionTreeClassifier(), LogisticRegression()]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vot.estimators_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vot.estimators_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'list'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vot.estimators_.__class__.__name__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 采用四个模型分别进行训练和测试集预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNeighborsClassifier 训练集的准确率： 0.8544285714285714 训练集的准确率 0.8013333333333333\n",
      "DecisionTreeClassifier 训练集的准确率： 1.0 训练集的准确率 0.753\n",
      "LogisticRegression 训练集的准确率： 0.8055714285714286 训练集的准确率 0.8126666666666666\n",
      "VotingClassifier 训练集的准确率： 0.8945714285714286 训练集的准确率 0.8103333333333333\n"
     ]
    }
   ],
   "source": [
    "# 对四个模型进行遍历\n",
    "for estimator in [knn,dt,lr,vot]:\n",
    "    # 采用当前的模型对数据进行训练\n",
    "    estimator.fit(Xtrain,Ytrain)\n",
    "    # 查看模型在测试集上的准确率\n",
    "    score_train = estimator.score(Xtrain,Ytrain)\n",
    "    score = estimator.score(Xtest,Ytest)\n",
    "    print(estimator.__class__.__name__,'训练集的准确率：',score_train,\n",
    "                                      '训练集的准确率',score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对比决策数据的准确率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入相关包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取数据集\n",
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(wine.data,wine.target,test_size=0.3,random_state = 260)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对比得分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(random_state=260).fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8888888888888888"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(random_state=260).fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 交叉验证结果对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AfxtjPvb8lwGNw0J49c5+9IyKoIGftse7KywkiJcm9GXLgTwKbB6JYwykpH7Hn5duY9eRUzx1QxwhQQ7rwHTDi19YHbALpgxwXgcsWJ2wkd2s4Yy+ICgErn/BqoJ5OscaKVR4GgrPWHV6Cs9YrwtOX7iv/LEFp6HE3RniUvkfjLI/EE3awZDfQ4Bnlgp1GrcyojFmKbC0wrbZ5T5fDXSq5Ly9QHwNY3TbgA7NautWjhcQIPRyQLMNQGJsE56N3Mnzy3ez79hZZk/sS9PwELvDctu+o2f415d7uSGhDf2d+DN2cCMc+BZG/K9vPbWKQOsapoeSonJ/IFwfC09d+PlF95221sI9e8J6RxQ70Jrs5Yfq9qOvqhVlI4M6tmjAbxZuYsysr3l1Uj86t3R+u70xhhkfZhASFMBjoxw2A7aM3TNh7RQYbPVJ1KRfoigf/t7FKrPsp4nege9Blb8qGxmUX1TqMyODlm07woodOTx0dSdaNHJYByxYT6ab3oHuN1gTlFT1BYdaNfu3fwTncu2Oxis00ata1TumCYunDSSmaRh3p6xzdPmG/KISZnyYQeeWDZiU3M7ucCrnS52wTpYwHorzIeN9uyPxCk30qtaVjQwa1t0q3/Do+5sdWb7hxS/2kH3CmgEb7MQOWHB1wnaFmAF2R+Lb2vS2ZhRveMvuSLzCoT+9yt+FhQTx4oQ+TB9yBfPX7ef2V9dwwkHlGzKPnWH2l3u4Pr6Nczv5D26C79OcNxPWF4lYT/Xfr4ccZwxL9iRN9Mo2AQHCr4d34dnbEtiwP5cbXlzF7iPOKN8w48OtBAcIv7/WoR2wYM2EDawHvW6zOxL/0Os2kEBI97+nek30ynY39I7i7XsGcKagmBtnpfKFzZ20y7YeZvn2Izx0dWdaOrEDFn7ohO1xg3bCekqDFtBpmFU/v6S46uN9iCZ65Qh9Y5uwePpVRDcN4xcp65i36jtbOmnzi0qY8VEGnVo04M6B7Wr9/m7L+AAKTmonrKf1ngCnD8Ge5XZH4lGa6JVjRDWuz8KpSQzt1pIZH27lsQ+2UFRSu520L32xh/3HzzFjTA/ndsACrJ8HzbtATJLdkfiXTsMhrJnfNd84+CdZ1UXh9YL418S+3De4I2+vzeKOV9eSe7Z2Ommzjp3lpS/3MDq+Dckdm9fKPS/Loc1Wp6F2wnpeUAjE3WrVzj973O5oPEYTvXKcgADhdyO68o9b40nLPMENs1ax+4j3VyZ68qMMqwPWqTNgy6S5OmHjx9odiX/qPQFKCq05Cn5CE71yrJv6RPP2lP6cyi/mxhdXsXLn5a9TUJXPtx1m2bYjPDi0E60iHNoBC1B4FjYtgO5jtBPWW1rFWf82vGl3JB6jiV45Wt/YpiyePpCoxvW5K2Udr6Xu8/g9rBmwW7miRQPuGtje49f3KO2ErR0JE+FgOhzOsDsSj9BErxwvukkYC+9LZkiXSP64JIP/XrTZo520//pyL1nHz/Lk9T2cX0I5bR407wyxyXZH4t/ibrFq4qf/2+5IPMLhP9VKWRrUC+Jftydy76AOvPlNFnfOW0ve2aIaX3f/8bO8+MVuru3VmuQrHNwBC3BoC2Sv007Y2hDeDLqMsJrJSmr+c2Y3TfTKZwQGCI+O7Mbfbu7F2u+Oc+OLq9ibU7NO2hkfbiUwQPhvJ8+ALfPtaxAYAvHj7I6kbkiYAGdyYNdndkdSY5rolc+5JbEt/75nALnnirhh1iq+3nX0sq6zYvsRlm07zINDO9E6or6Ho/SwwrPWjE3thK09V1wN4S38Yky9W4leREaIyA4R2S0ij1Syv4mIfCAim0RkrYj0dPdcpS5Hv3ZNWTxtIK0j6jNp3lre+CazWufnF5XwxIcZdIwM5xdO74AF2LoICvK0E7Y2BQZbi7ns/BjOXN7DhFNUmehFJBCYBYwEugPjRKR7hcMeA9KNMb2AO4DnqnGuUpelbdMwFt6XxKDOkfxh0RYeX7yFYjc7aees3EvmsbPMuL6n8ztgwZoJ26yTtdydqj0JE6C02Kor5MPc+Qm/EthtjNlrjCkE5gNjKhzTHfgcwBizHWgnIi3dPFepy9YwNJiX70jknp+05/XVmdyVsq7KTtr9x88ya8Vuro1rzVWdHN4BC9YQv+y12glrh5bdrVr1Pj76xp1EHwXsL/c627WtvI3ATQAiciUQC0S7eS6u86aIyHoRWZ+T472JMcr/BAYIv7+2O0//vBff7D3GjS+t4rujZy56/J8+2kqAOLwEcXlp2glrq4QJcHiztQi7j3In0Vf2CFGxrOBfgSYikg48AGwAit0819pozBxjTKIxJjEyMtKNsJS60K392vLm3f05caaQG2atInX3j9tVV+w4wqdbD/PA0Cto09jhHbDgmgk7H7pdbw35U7Wv58+tP7Q+/FTvTqLPBtqWex0NHCh/gDHmpDHmLmNMAlYbfSTwnTvnKuVJ/Ts0Y/G0q2jRsB53zF3LW2t+6KQtKC5hxpIMOjQPZ/JVHWyMshq2LoZ87YS1VVhT6Hqt1U5f7JxV0KrDnUS/DugkIu1FJAQYCywpf4CINHbtA5gMrDTGnHTnXKU8LaZZGO/fn8xVnZrz+w+28MSSDIpLSnl55V72HTvLE74wA7ZMWgo0uwLaXWV3JHVbwgQ4d9wageODgqo6wBhTLCLTgU+AQGCuMSZDRKa69s8GugGvi0gJsBW4+1LneudLAYoLvHbpapEAa2iWsk3D0GBendSP/1m6jVe//o6dh0/xbdYJRvZsxU87+0jT4JFtsP8bGPaUdsLarePPoGFra0x99+vtjqbaqkz0AMaYpcDSCttml/t8NdDJ3XO95n/bQdHZWrnVJUkgTHwPOg6xOxL7pf8bVvwFbvpXrddnCQwQ/nBdd65o0YA/LNpCcGAA/32dD43uTUtxdcKOtzsSFRBorSmb+gKcOgwNW9odUbW4leh9xuBHodQBdSlWPmO9xdNED1veh7wseO16GP2cVeu7lo27MoaebSLILy4hyhc6YAGKzsHGt6HbaO2EdYqECbDqWav+zcAH7Y6mWvwr0Tvlm7/3C8hcZXcU9istgf1roMdNcPYYLL4fcrbD1U9YT0i1KC46olbvV2PaCes8kZ0hup/1LjX5AZ9qTvORHikfE5NsVRrMz7M7Ensd3mLVTu8yymrK6jcZUp+H+ROg4JTd0TlbWgo07QDtfmJ3JKq8hAmQsw0OfGt3JNWiid4bYpMBA/vX2h2JvTJXWx9jk6zO6Wv/DqOegV2fwqvD4UT16tPUGUe2QdZqnQnrRD1vgqBQnxtTr4neG6L7QUCQNt9kroKIGIiI/mHblffAxIWQlw0v/wyyvrEvPqdKe81a9CKh9vszVBVCI6x+k83vQlG+3dG4TRO9N4SEQeuEH55o6yJjrKfSykbadPwZTF4GoY3gtdGQ/nbtx+dUF3TC+kAdnrooYbzVLLujdgYTeoImem+JTbba8YrO2R2JPY7tsRZtiE2qfH9kZ5j8ObTtD4umwmd/hFLPLQ/os7Yugfxc7YR1svaDoFG0T9Wp10TvLbHJUFII36fZHYk9ypqtYi4xdj6sKdz+AfS9yxq2tmAiFNRsxSifp52wzhcQCPFjYc9yOOkbFV000XtL2/7Wx7rafJO1GsKaQ/NK59H9IDAYrvsnjHwadv4fzB0BufsvfY6/OrIdslKhzyQI0F9NR0sYD6YUNs63OxK36E+Tt4Q1hRY9rF/cuigz1Wq2cWfUiAj0vxcmvAu5mfDykLo5Yulb7YT1Gc06QkyS1XxjKi3I6yia6L0pNslKWCXFdkdSu/K+txL2pZptKnPF1VYnbUgDSLnO51f1qZaifGvIXrfroIGP1OKp6xImwLHdkL3O7kiqpInem2KSoPA0HNpkdyS1K6vc+PnqiuwC9yyHtlfC+/fA50/WjU7abdoJ63N63ADBYbDhTbsjqZImem8qG1qYVcfa6TNTIaQhtIy7vPPDmsLE96226q/+Du/cDoUXXzHKL6SlQJP20O6ndkei3FWvIXQfAxkfWAvEOJgmem9q1AaatLMSX12Stdp6Ig+sQSmloBCrCNrwv1jjlecOtyZZ+aOcndYopb7aCetzEiZYZT62f2R3JJekP1XeFpNsJT4f6LDxiLPH4cjWy2u2qUgEku6H8e/A8X0wZwhkr6/5dZ0mLcXVCTvR7khUdcUOhMYxjh9Tr4ne22KTrcqNR3faHUntKCtpEDvQc9fsdA1M/gyC68O8UbB5oeeubbeifNj4b2upOu2E9T0BAdZT/d4vHT0s2K1ELyIjRGSHiOwWkUcq2R8hIh+KyEYRyRCRu8rt2ycim0UkXUT88HGsCmXt9HWl+SYr1Voso00fz163RTe4ZwVEJ8J7d8PyP/tHJ+22D+HcCe2E9WXxYwHj6DH1VSZ6EQkEZgEjge7AOBGpuEzPNGCrMSYeGAz8vdwasgBDjDEJxphEz4TtQ5p2gPAWdSfRZ6ZCVF8IDvX8tcObwe2LoPdEWPk0vDvJ9ztp01Ksfpz2g+yORF2uJu2smcwOHlPvzhP9lcBuY8xeY0whMB8YU+EYAzQUEQEaAMeBOjZ4/CJErKf6ujDypvAMHNzo3SUDg0Lg+pkw7M/W0/C8kT4zDf1Hju6CzK91Jqw/SJgAJ75z7O+5Oz9dUUD5xqds17byZmItEH4A2Az80hhT9r7aAJ+KSJqITLnYTURkioisF5H1OTk5bn8BPiE2GfL2Q26W3ZF4V/Y6KC2u/kSp6hKB5OkwfoFVPG3OEN+sKZSWYpWz1pmwvq/79dZEvw3O7JR1J9FXNoe94vuT4UA60AZIAGaKSCPXvoHGmD5YTT/TRKTSgcLGmDnGmERjTGJkpJ91SsW4RqD4e92bzFSQAGtoZW3oPBzu/sx6yp83ylqf1leUzYTteq3PLTStKhESbk2gyvjAkYX53En02UDbcq+jsZ7cy7sLeN9YdgPfAV0BjDEHXB+PAB9gNQXVLS17QL1G/l/3JjMVWsVZdeZrS8vuVidtm96w8C5Y8RfHtpNeYPtHcO64dsL6k4SJUHTGmuXsMO4k+nVAJxFp7+pgHQtU/EqygKEAItIS6ALsFZFwEWno2h4ODAO2eCp4nxEQCDED/PuJvrjQGuPu7WabyoQ3hzsWW00gX/7VSvgOn6lIWgo0joX2g20ORHlMzABr8IUDlxmsMtEbY4qB6cAnwDbgHWNMhohMFZGprsP+BCSLyGbgc+B3xpijQEvgaxHZCKwF/mOM+dgbX4jjxSTB0R1w5qjdkXjHwXQoPueZiVKXI6gejJkF1zwJGYsgZRScPGhPLFU5uhv2faUzYf2NiFW+eN9XcPw7u6O5gFs/ZcaYpcaYzsaYjsaYP7u2zTbGzHZ9fsAYM8wYE2eM6WmMedO1fa8xJt71r0fZuXWSv9e9KRs+GmNTogfrF23gL2Hc29aIlpeHwIEN9sVzMWnzXJ2wOhPW78SPA8RxY+r1caK2tOltrR7vr803WauhWSdo0MLuSKDLSLj7U6uswNyR1hO+UxQXWG/tu4zSTlh/FBENHQZb/8cOmtCnib62BNWDqMQfltjzJ6WlroXAbXyar6hlD6vccete1sSqL592Rifttg+1E9bfJUyAvCxrjoRD1KC8oKq22CSr7G7BKavEqb84shXy8+zpiL2UBpEw6UP48Jew4s9WjZyQcHtjys2yimB1GGJvHMp7ul1njbLb8Ba0d0bZaU30tSk2GVb+zVp16oqhdkfjOecXGnFYogfrndQNL1lNZ7s+szsaCGumnbD+Lrg+9LwJNi6AUX+r3eHGF6GJvjZFXwkSaHVc+lOiz1wFjaKsJ1UnKluTtv+9dkei6oqEidYQ2q2LoM8ddkejbfS1ql4Dq83Yn0beGGN1MMe4uRC4UnVBdKI1OMEhJRE00de22IHWxKLiArsj8YwT38HpQ85stlHKLiLQewLs/8aqx2QzTfS1LSYJSgqcOb77cmQ6uH1eKTv1GmvVfnLA6lOa6Gvb+QJnfjLMMjMV6jeB5l3sjkQpZ2nUGjoOtSZPlZbYGoom+toW3gwiu/rPxKmsVGtYpY4iUerHEsbDye9h7xe2hqG/nXaISYL9a2z/K19jpw7B8b3OmiillJN0GQWhjW0vdKaJ3g6xyVBwEg77eCHP8/VttH1eqUoFh0Lcza6y1Lm2haGJ3g7nFwz38eabrNUQHG4NGVVKVS5hAhTnQ4Z9C+NoordDRDRExPj+QiSZq6FtPwgMtjsSpZyrTW+I7GbrmHpN9HaJTbKaPpxQaOtynMu1mp602UapSysbU//9esjZYUsImujtEpMEZ3IcMZnisuxfAxgdP6+UO3rdZpU/sWlMvSZ6u8QOtD76avNNZqpV7z060e5IlHK+Bi2g0zCr0FlJca3f3q1ELyIjRGSHiOwWkUcq2R8hIh+KyEYRyRCRu9w9t85q3gnCmv8wcsXXZKZabY/B9e2ORCnf0HuCVS5kz/Jav3WViV5EAoFZwEigOzBORLpXOGwasNUYEw8MBv4uIiFunls3ibgWDPfBRF90zirhoOPnlXJfp+FWmWobmm/ceaK/EtjtWv+1EJgPjKlwjAEaiogADYDjQLGb59ZdsQMhNxPyvrc7kurJXg+lRT80PymlqhYUAnG3wo6lcPZ4rd7anUQfBewv9zrbta28mUA34ACwGfilMabUzXMBEJEpIrJeRNbn5OS4Gb6PK3si9rWyxZmpgEDb/nZHopRvSRgPJYWw5b1ava07ib6yIuMVxwQOB9KBNkACMFNEGrl5rrXRmDnGmERjTGJkZKQbYfmBlnEQ0sD3mm+yUq01Wes3tjsSpXxL617QKg42vFmrt3Un0WcDbcu9jsZ6ci/vLuB9Y9kNfAd0dfPcuiswyHoq9qUn+pIi2L9Oh1UqdbkSJsDBdDicUWu3dCfRrwM6iUh7EQkBxgJLKhyTBQwFEJGWQBdgr5vn1m2xSdbi2rXcZnfZDm6CojM/lFtWSlVP3K3W0ORaLHRWZaI3xhQD04FPgG3AO8aYDBGZKiJTXYf9CUgWkc3A58DvjDFHL3auN74Qn1U2szTrG3vjcFfZuH99olfq8oQ3g87DYdMC6x1yLXBrcXBjzFJgaYVts8t9fgAY5u65qpyovhAYYiXQrqPsjqZqmauhaQdo2MruSJTyXb0nWhUtd31WK7/3OjPWbsGhVrL3hQ7Z0tIfFhpRSl2+K66G8MhaG1Ovid4JYpLg4EYoPGN3JJd2dAecO6ETpZSqqcBgq/7Nzo/hzFGv304TvRPEDoTSYsheZ3ckl5ap7fNKeUzCBOv3ftM7Xr+VJnonaHultVq80xciyVoNDVpBk/Z2R6KU72vZ3aoXVQujbzTRO0FoI2jZEzJX2R3JxRljPdHHJll1epRSNZcwAQ5vtppuvUgTvVPEJls1ZIoL7Y6kcrlZ1mr2Wt9GKc/p+XNr1J2Xn+o10TtFbDIUn/P6X/bLVjZ7VydKKeU5YU2hyyirnd6LD3ma6J2iLIE6tfkmcxWERkALrTKtlEf1ngjnjlsjcLxEE71TNGgBza5wbt2bzNXWH6MA/ZFRyqM6DLEGOXhxTL3+1jpJbLKV6EtL7Y7kQqdz4NgubbZRyhsCgyB+rDVL9tRhr9xCE72TxCRDfp5V5MxJtL6NUt6VMAFMiVX/xgs00TuJUxciyVwNQfWhdYLdkSjlnyI7Q3Q/a/SNqXTJjhrRRO8kjWOhUZTz6t5kpUJ0orUUmlLKOwbcB12v9UpFS030TiJitYNnpnrlr/plyT8JhzZrs41S3tbz5zD0D155oNJE7zSxSXD6EJz4zu5ILPvXginVjlilfJgmeqcpm3nqlLo3WakQEGTV41FK+SS3Er2IjBCRHSKyW0QeqWT/b0Qk3fVvi4iUiEhT1759IrLZtW+9p78Av9O8C9Rv4px2+sxUaB0PIeF2R6KUukxVJnoRCQRmASOB7sA4EblgeqQx5m/GmARjTALwKPClMab8IqhDXPsTPRe6nwoIsJpJshyQ6Ivy4fs0bbZRyse580R/JbDbGLPXGFMIzAfGXOL4ccDbngiuzopNhuN74dQhe+M48C2UFGohM6V8nDuJPgrYX+51tmvbj4hIGDACeK/cZgN8KiJpIjLlYjcRkSkisl5E1ufk5LgRlh8rW6rP7uabsro7MQPsjUMpVSPuJPrKio9fbOzfaGBVhWabgcaYPlhNP9NE5KeVnWiMmWOMSTTGJEZGRroRlh9r3QuCw+yfOJW5GiK7WRX2lFI+y51Enw20Lfc6GjhwkWPHUqHZxhhzwPXxCPABVlOQupTAYGuUi50jb0qKraGVOn5eKZ/nTqJfB3QSkfYiEoKVzJdUPEhEIoBBwOJy28JFpGHZ58AwYIsnAvd7MclweAucy7Xn/oc3Q+EpTfRK+YEqE70xphiYDnwCbAPeMcZkiMhUEZla7tAbgU+NMWfKbWsJfC0iG4G1wH+MMd4ruuxPYpMAA/vX2HP/TF1oRCl/EeTOQcaYpcDSCttmV3idAqRU2LYXiK9RhHVVVCIEBFsdsp2H1/79s1Kt2jsRlfa7K6V8iM6MdaqQMGuFeDs6ZI2xnui12UYpv6CJ3slik+D7b6HoXO3e9+guOHtUm22U8hOa6J0sJhlKiyC7litH6EIjSvkVTfROFtMfkNpvvslcDeGR1hq2Simfp4neyeo3gZY9fpihWlsyU61mG6lsrpxSytdoone6mCTYv86awFQb8rIhL0ubbZTyI5ronS42GYrOwKGNtXO/svHzmuiV8hua6J0utpYLnGWugnqNoGXP2rmfUsrrNNE7XcNW0KR97dW9yVpt1dkJCKyd+ymlvE4TvS+IHWgl4NJS797nzDHI2a7NNkr5GU30viA2Cc4dh6M7vHufsmGcMZrolfInmuh9QdkMVW+302ethsB6ENXHu/dRStUqTfS+oGkHaNDK+xOnMlMhOhGC6nn3PkqpWqWJ3heIWM03malWwTFvKDgNBzdqfRul/JAmel8Rkwwnv4fcLO9cP3stmBJXHXyllD/RRO8rykbCeKv5JnM1SAC07e+d6yulbKOJ3le06A6hEd6re5OZCq16Qb2G3rm+Uso2biV6ERkhIjtEZLeIPFLJ/t+ISLrr3xYRKRGRpu6cq9wUEABtB3hn4lRxAXy/XsfPK+Wnqkz0IhIIzAJGAt2BcSLSvfwxxpi/GWMSjDEJwKPAl8aY4+6cq6ohNgmO7YLTOZ697oF0KM7XRK+Un3Lnif5KYLcxZq8xphCYD4y5xPHjgLcv81x1KbEDrY9ZHh5PX9YcpCNulPJL7iT6KGB/udfZrm0/IiJhwAjgvcs4d4qIrBeR9Tk5Hn5i9RetEyCovuebb7JWQ/POEN7cs9dVSjmCO4m+stUnLjaYezSwyhhzvLrnGmPmGGMSjTGJkZGRboRVBwWFWBOaPPlEX1oCWWv0aV4pP+ZOos8G2pZ7HQ0cuMixY/mh2aa65yp3xCbDoc2Qf9Iz1zuyFQryfmgWUkr5HXcS/Tqgk4i0F5EQrGS+pOJBIhIBDAIWV/dcVQ0xSWBKYf9az1yvrH6OTpRSym9VmeiNMcXAdOATYBvwjjEmQ0SmisjUcofeCHxqjDlT1bme/ALqnOh+IIGea77JTIVG0dA4xjPXU0o5TpA7BxljlgJLK2ybXeF1CpDizrmqBuo1gDYJnumQNcbqiG0/qObXUko5ls6M9UUxSdYEp6L8ml3n+F44fVibbZTyc5rofVFsMpQUwoFva3adsvZ5XWhEKb+mid4XeWohkqzVENYMIrvUPCallGNpovdFYU0hslvNE33mKuuPhlQ23UEp5S800fuq2CRriGVpyeWdf/IgnNinE6WUqgM00fuq2IFQeMqaPHU5yoZnaiEzpfyeJnpfVdN2+sxUCGlg1aBXSvk1TfS+KiLKmuR0uROnMldbk68C3ZpKoZTyYZrofVnsQCthV3fB8LPHrRo3Wt9GqTpBE70vi0mCs0fh6K7qnbd/DWB0opRSdYQmel92fsHwajbfZKZCQDBE9fV8TEopx9EGWl/W7AoIj7Sab/re6f55Washqg8E1/daaMr3FBUVkZ2dTX5+DUtrKK8KDQ0lOjqa4OBgt8/RRO/LRKzmm+qMvCk8Awc2QPID3otL+aTs7GwaNmxIu3btEJ1E50jGGI4dO0Z2djbt27d3+zxtuvF1scmQlwV52e4dn70eSou1vo36kfz8fJo1a6ZJ3sFEhGbNmlX7XZcmel93fjy9m2WLs1YDAm2v9FpIyndpkne+y/k/0kTv61rFQUhDq26NOzJXQaueUL+xV8NSSjmHW4leREaIyA4R2S0ij1zkmMEiki4iGSLyZbnt+0Rks2vfek8FrlwCAiGmv+tJvQolRVbTjTbbKIcKDAwkISGBnj17Mnr0aHJzcz1y3ZSUFKZPn+6Ra7Vr1464uDgSEhJISEggNdVDq71VkJ6eztKlnlmzqcpELyKBwCxgJNAdGCci3Ssc0xh4EbjeGNMDuKXCZYYYYxKMMYkeiVpdKCYJcrZbE6Eu5eBGKDqr4+eVY9WvX5/09HS2bNlC06ZNmTVrlt0hVWrFihWkp6eTnp5OcrJ7D07FxcXVuocnE707o26uBHYbY/YCiMh8YAywtdwx44H3jTFZAMaYIx6JTrmnbIZr1mroeu3Fj9OFRpSbZnyYwdYDJz16ze5tGvHH0T3cPj4pKYlNmzYBsHbtWh566CHOnTtH/fr1mTdvHl26dCElJYUlS5Zw9uxZ9uzZw4033sjTTz8NwLx58/jLX/5C69at6dy5M/Xq1QMgMzOTX/ziF+Tk5BAZGcm8efOIiYnhzjvvpH79+mzfvp3MzEzmzZvHa6+9xurVq+nfvz8pKSkXjfVS12zatCkbNmygT58+3H///UybNo2cnBzCwsJ4+eWX6dq1K++++y4zZswgMDCQiIgIli1bxuOPP865c+f4+uuvefTRR7ntttsu+3vvTqKPAvaXe50N9K9wTGcgWES+ABoCzxljXnftM8CnImKAfxlj5lR2ExGZAkwBiInRhaqrJaoPBNazEnlVib5pR2jYsvZiU+oylJSU8Pnnn3P33XcD0LVrV1auXElQUBDLli3jscce47333gOsJ98NGzZQr149unTpwgMPPEBQUBB//OMfSUtLIyIigiFDhtC7d28Apk+fzh133MGkSZOYO3cuDz74IIsWLQLgxIkTLF++nCVLljB69GhWrVrFK6+8Qr9+/UhPTychIQGAIUOGEBgYSL169VizZs0lr7lz506WLVtGYGAgQ4cOZfbs2XTq1Ik1a9Zw//33s3z5cp588kk++eQToqKiyM3NJSQkhCeffJL169czc+bMGn8/3Un0lXXxViyuEgT0BYYC9YHVIvKNMWYnMNAYc0BEWgCfich2Y8zKH13Q+gMwByAxMbGaxVvquKB61izXS42nLy21nvi7XVd7cSmfVZ0nb086d+4cCQkJ7Nu3j759+3LNNdcAkJeXx6RJk9i1axciQlFR0flzhg4dSkREBADdu3cnMzOTo0ePMnjwYCIjIwG47bbb2LlzJwCrV6/m/fffB+D222/nt7/97flrjR49GhEhLi6Oli1bEhcXB0CPHj3Yt2/f+US/YsUKmjdvfv68S13zlltuITAwkNOnT5Oamsott/zQsl1QUADAwIEDufPOO7n11lu56aabPPCdvJA7nbHZQNtyr6OBA5Uc87Ex5owx5iiwEogHMMYccH08AnyA1RSkPC022WqDLzhd+f6c7ZCfq4XMlKOVtdFnZmZSWFh4vo3+D3/4A0OGDGHLli18+OGHF4wjL2uSAaszt6wt3N1hiOWPK7tWQEDABdcNCAioVht7+WuGh4cDUFpaSuPGjc+37aenp7Nt2zYAZs+ezVNPPcX+/ftJSEjg2LFjbt/LHe4k+nVAJxFpLyIhwFhgSYVjFgM/EZEgEQnDatrZJiLhItIQQETCgWHAFs+Fr86LTQJTAtlrK99fNvxSV5RSPiAiIoLnn3+eZ555hqKiIvLy8oiKigK4ZFt5mf79+/PFF19w7NgxioqKePfdd8/vS05OZv78+QC89dZbXHXVVTWO151rNmrUiPbt25+PxRjDxo0bAdizZw/9+/fnySefpHnz5uzfv5+GDRty6tSpGscGbiR6Y0wxMB34BNgGvGOMyRCRqSIy1XXMNuBjYBOwFnjFGLMFaAl8LSIbXdv/Y4z52CORqwtFXwkScPGJU1mroWFraNKuVsNS6nL17t2b+Ph45s+fz29/+1seffRRBg4cSElJ1ctntm7dmieeeIKkpCSuvvpq+vTpc37f888/z7x58+jVqxdvvPEGzz33XI1jdfeab731Fq+++irx8fH06NGDxYsXA/Cb3/yGuLg4evbsyU9/+lPi4+MZMmQIW7duJSEhgQULFtQoPjHVrWVeCxITE8369Trkvtr+NQjqNYQ7P7pwuzHwj+7WU//Nc+2JTTnetm3b6Natm91hKDdU9n8lImkXG8KuM2P9SWwyZK+D4oILt5/YB6cOaLONUnWUJnp/EpMExflwIP3C7WWzZnUhcKXqJE30/uRiC5FkpkJoY4jUt+VK1UWa6P1JeHNo3vnH4+kzU62n/QD971aqLtLffH8TkwRZa6DUNTLh1GE4vkfr2yhVh2mi9zexyVCQB0dcpYjK2ue1vo1SdZYmen9T1k5f1nyTmQrBYdA63r6YlHJTWZniHj16EB8fzz/+8Q9KS0sv61qPP/44y5Ytu+j+2bNn8/rrr190vzs2b958vlxx06ZNad++PQkJCVx99dU1uq6n6Zqx/qZxDDSKthJ8/3utjtnoRAgKsTsypapUVgIB4MiRI4wfP568vDxmzJhR7Ws9+eSTl9w/derUywnxAnFxcefjvfPOO7nuuuu4+eabLzimuLiYoCB7U60men8UmwTfrYRzuXBoCwz6nd0RKV/zf4/Aoc2evWarOBj5V7cPb9GiBXPmzKFfv3488cQTlJaW8sgjj/DFF19QUFDAtGnTuPfeewF4+umneeONNwgICGDkyJH89a9/vSDxPvLIIyxZsoSgoCCGDRvGM888wxNPPEGDBg349a9/TXp6OlOnTuXs2bN07NiRuXPn0qRJEwYPHkz//v1ZsWIFubm5vPrqq/zkJz+pMvbBgweTnJzMqlWruP766xk8eDAPP/wwp0+fpnnz5qSkpNC6dWv27NlTadliT9NE749ik2Hzu7BxPmB0/LzyWR06dKC0tJQjR46wePFiIiIiWLduHQUFBQwcOJBhw4axfft2Fi1axJo1awgLC+P48QsX4Dl+/DgffPAB27dvR0QqXbXqjjvu4IUXXmDQoEE8/vjjzJgxg2effRawnsjXrl3L0qVLmTFjxiWbg8rLzc3lyy+/pKioiEGDBrF48WIiIyNZsGABv//975k7dy5TpkyptGyxp2mi90dlHa+pz0NAEET3szce5Xuq8eTtbWVlWj799FM2bdrEwoULAat08a5du1i2bBl33XUXYWFhADRt2vSC8xs1akRoaCiTJ0/m2muv5brrLizVnZeXR25uLoMGDQJg0qRJF5QSLisb3LdvX/bt2+d23GULhezYsYMtW7acL7lcUlJC69atL1m22NM00fujyC5Qvymc/B6iEiEkzO6IlLose/fuJTAwkBYtWmCM4YUXXmD48OEXHPPxxx9fsiRxUFAQa9eu5fPPP2f+/PnMnDmzWk/NZeWKy5dAdkdZeWJjDD169GD16gsLDp48efJ82WJv01E3/kjkh+YabbZRPionJ4epU6cyffp0RIThw4fz0ksvnV90ZOfOnZw5c4Zhw4Yxd+5czp49C/CjppvTp0+Tl5fHqFGjePbZZ3+UWCMiImjSpAlfffUVAG+88cb5p3tP6NKlCzk5OecTfVFRERkZGZcsW+xp+kTvr2KSYPtHmuiVTylbYaqoqIigoCBuv/12Hn74YQAmT57Mvn376NOnD8YYIiMjWbRoESNGjCA9PZ3ExERCQkIYNWoU//M//3P+mqdOnWLMmDHk5+djjOGf//znj+772muvne+M7dChA/PmzfPY1xQSEsLChQt58MEHycvLo7i4mIceeogePXrw1ltvcd999/HUU09RVFTE2LFjiY/3/FBoLVPsr07nwKpn4Wd/gOBQu6NRPkDLFPuO6pYp1id6f9UgEob/2e4olFIO4FYbvYiMEJEdIrJbRB65yDGDRSRdRDJE5MvqnKuUUsp7qnyiF5FAYBZwDdYi4OtEZIkxZmu5YxoDLwIjjDFZItLC3XOVUs5hjHF7UW1lj8tpbnfnif5KYLcxZq8xphCYD4ypcMx44H1jTJYrkCPVOFcp5QChoaEcO3bsshKJqh3GGI4dO0ZoaPX63dxpo48C9pd7nQ30r3BMZyBYRL4AGgLPGWNed/NcpZQDREdHk52dTU5Ojt2hqEsIDQ0lOjq6Wue4k+grex9X8U9+ENAXGArUB1aLyDdunmvdRGQKMAUgJibGjbCUUp4UHBxM+/bt7Q5DeYE7TTfZQNtyr6OBA5Uc87Ex5owx5iiwEoh381wAjDFzjDGJxpjEyMhId+NXSilVBXcS/Tqgk4i0F5EQYCywpMIxi4GfiEiQiIRhNc9sc/NcpZRSXlRl040xplhEpgOfAIHAXGNMhohMde2fbYzZJiIfA5uAUuAVY8wWgMrO9dLXopRSqhKOnBkrIjlApt1x1FBz4KjdQTiEfi8upN+PC+n34wc1+V7EGmMqbfd2ZKL3ByKy/mLTkesa/V5cSL8fF9Lvxw+89b3Q6pVKKeXnNNErpZSf00TvPXPsDsBB9HtxIf1+XEi/Hz/wyvdC2+iVUsrP6RO9Ukr5OU30Sinl5zTRe5CItBWRFSKyzVWX/5d2x2Q3EQkUkQ0i8pHdsdhNRBqLyEIR2e76GUmyOyY7ich/uX5PtojI2yJSp5ZCE5G5InJERLaU29ZURD4TkV2uj008cS9N9J5VDPzKGNMNGABME5HuNsdkt19ilcNQ8BxWTaiuWLWg6uz3RUSigAeBRGNMT6yZ82PtjarWpQAjKmx7BPjcGNMJ+Nz1usY00XuQMeagMeZb1+ensH6Ro+yNyj4iEg1cC7xidyx2E5FGwE+BVwGMMYXGmFxbg7JfEFBfRIKAMC5S8NBfGWNWAscrbB4DvOb6/DXgBk/cSxO9l4hIO6A3sMbmUOz0LPBbrPpHdV0HIAeY52rKekVEwu0Oyi7GmO+BZ4As4CCQZ4z51N6oHKGlMeYgWA+OQAtPXFQTvReISAPgPeAhY8xJu+Oxg4hcBxwxxqTZHYtDBAF9gJeMMb2BM3jobbkvcrU9jwHaA22AcBGZaG9U/ksTvYeJSDBWkn/LGPO+3fHYaCBwvYjsw1pC8mci8qa9IdkqG8g2xpS9w1uIlfjrqquB74wxOcaYIuB9INnmmJzgsIi0BnB9PFLF8W7RRO9BYq2q/CqwzRjzD7vjsZMx5lFjTLQxph1WJ9tyY0ydfWIzxhwC9otIF9emocBWG0OyWxYwQETCXL83Q6nDndPlLAEmuT6fhLXWR425s5Sgct9A4HZgs4iku7Y9ZoxZal9IykEeAN5yLcKzF7jL5nhsY4xZIyILgW+xRqttoI6VQhCRt4HBQHMRyQb+CPwVeEdE7sb6Y3iLR+6lJRCUUsq/adONUkr5OU30Sinl5zTRK6WUn9NEr5RSfk4TvVJK+TlN9Eop5ec00SullJ/7fwpwX/f40e7PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=25)\n",
    "rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10) #折10次\n",
    "\n",
    "clf = DecisionTreeClassifier()\n",
    "clf_s = cross_val_score(clf,wine.data,wine.target,cv=10) #折10次\n",
    "\n",
    "\n",
    "plt.plot(range(1,11),rfc_s,label='RandomForest')\n",
    "plt.plot(range(1,11),clf_s,label='Decision Tree')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 结论：在任意情况下随机森林都要比决策树的得分高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制随机森林-n_estimators 学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = []\n",
    "\n",
    "for i in tqdm(range(3,100)):\n",
    "    #n_jobs CPU的核数\n",
    "    rfc =  RandomForestClassifier(n_estimators=i,n_jobs=-1,random_state=260)\n",
    "    # 交叉验证\n",
    "    rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10).mean()\n",
    "    score_list.append(rfc_s)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OUJSSSaev/VVEn3hkr3Y3F++HAwCoNN87d001Po8+fO/OnF1zIBLTc2dGMzr2bwaj+tgDdxVlf6O0R/a1qsbnkb+hWgfaGgpdzrbrC7bpj18e0a9+59VClwIAQF55PR79i088UOgy8o7gCEXp1dEJ/cvvva3E/KL+yU/dV+hyAABabkz9v/3JG2qs9eU0OPpX339br41Oyt9QveGxLfVV+vjhvTl77XyorfLqFx7dL/+O6qIOuPLlicN79IMf3dBfv8OoIwBAeavyVsYkLoIjFKX0Ur79gzH9k58qcDEAAElSZOyWbtyc042bc7o2Oau7mmu3fM3puUWduTShp368W09/tHy+KPhff/pgoUsomCNdfv31r/5EocsAAAA5UhnxGEpOuvnpa6MTmppdKHA1AADp3VBfkgaGcjOa5NSFuBaTruJ6AQEAAJQKgiMUndmFJZ2+MK5De5uUdNKp4XihSwIA6N3G1C31VQoP5mbVrIFITFVe09Euf06uBwAAgNwiOELReeXSuOYWk/qlD/eoxudRf44+nAAAsreUdBoYiqmvJ6De7oDCkZicc1u+bn8kqof3taqu2puDKgEAAJBrBEcoOgORmLwe04/d3aajXf6VaWsAgMJ56+qUJmcWFAq2KRQM6PLEjC7FE1u65kRiXueuTDFNDQAAoIgRHKHohCMxPbC3WY21VeoNBnT+2k3Fbs0VuiwAqGjpEL83GFBvsC21bWsjQl8aiss5qa+nbcv1AQAAID8IjlBUbs0t6tWRCfX1LH/7nP4wMTDEdDUAKKT+wZh6du7QrqZaBdsbtKupZsvBUTgSVV2VVw91tOSmSAAAAOQcwRGKyqnh9Oo6y4HRoT1NaqzxbfnDCQAge/OLSZ26EF+ZUmZmCgXbNBCJbqnPUTgS07EDflX7+HUEAACgWPGbGopKOBJVtc+jD+5vlST5vB4d7/ZrgOAIAArmtdEJJeaX3tOLqDcYUPTWvH50/VZW17wxNavBG7fobwQAAFDkCI5QVPoHY/rgvlbVVr27uk5vsE3D0WldmZgpYGUAULn6B2Mykx7tfjfkSQc+2S5gkB5Jmh5hCgAAgOJEcISiMT49rzevvn91nXS/I6arAUBhhCNRfWBPk1rqq1e2dbTWa3+gXv2D2f23ORyJqrmuSgf3NOWqTAAAAOQBwRGKxkupBtihnvcGR/fsbFSgoVrhwey+1QYAZG9mfklnLk2sOzIoFAzoxFBMi0vJTV83HInp0W6/vB7LRZkAAADIE4IjFI3+SFQN1V49uGZ1HY/H9GgwoHAktqUmrACAzTt9Ma75peS6vYhCwTbdnFvUuStTm7rmpVhCo+MzTFMDAAAoAQRHKBrp1XWqvO//Z9kXbNO1qVkNR6cLUBkAVK5wJCafx3S0y/++femeR/2b7HOU7ovU10NjbAAAgGJHcISicG1yVkNj0+rrWf/b5/Q33f30OQKAbRWOxHS4s0UNNb737WtvrNG9uxo3vfJlOBJTe2ONgu07clUmAAAA8oTgCEUh/e1z722WZd4fqNee5loNZLl6DwBg8yZnFvT66IRCtwn1peW+dKcuxDW3uJTRNZ1zCkdiCgUDMqO/EQAAQLEjOEJRCEdiaq2v0v13rb+6jpkp1NOmgUhMySR9jgBgO5wcjivptG5/o7RQsE2zC0mduTSR0TXfuXFL0Vtz6qO/EQAAQEkgOELBOecUHoyqNxiQ5w6r64SCAY0nFvTWtc01YQUAZCcciaq2yqOH97Xc9phjB/zy2PIXAJnoH7zzCFMAAAAUF4IjFNzFWEJXJmfVu8G3z+nVdzbbSwMAkJ3wYExHu/yq8Xlve0xzXZUe6GjJeCpxOBLTPn+9Ov31uSoTAAAAeURwhIJLf0t9p6kQknRXc6262xsy/lYbAJC9sZtzevv6zYxGBoWCAZ25NKHpucU7HreUdHppKLbhf+8BAABQPDIKjszscTN728wGzezpdfa3mtlzZvaamZ00s0Or9v2KmZ0zszfM7FtmVpva7jez75vZO6mfrbl7Wygl/ZGo7mqqVXdbw4bHhoIBnRiKaWEpuQ2VAUDlemloOaTPpBdRX7BNi0mnUxfidzzujcuTujm7yDQ1AACAErJhcGRmXklflfRRSQclfdLMDq457MuSzjrnHpT0KUlfSZ27V9I/kHTEOXdIklfSk6lznpb0onPubkkvpp6jwiSTTi9tYnWdULBN0/NLem10chuqA4DKFY5E1Vjr0wf2rL9owWof3N+qaq9nw6nE744wpTE2AABAqchkxNExSYPOuSHn3Lykb0t6Ys0xB7Uc/sg5d15Sl5ntSu3zSaozM5+keklXUtufkPTN1ONvSvp4tm8CpetHN24qNj2f8bfPvd3Lx4UHM+ulAQDITjgS0/EDAfm8G/+qUFft1cP7WtS/QZ+jcCSqe3btUHtjTa7KBAAAQJ5lEhztlTSy6vloattqr0r6hCSZ2TFJ+yV1OOcuS/otSZckXZU06Zz7i9Q5u5xzVyUp9XNntm8Cpat/MPXtc09m3z63NlTr4O4m+hwBQB6Njid0MZZQX0/mU8r6etp07sqUJhLz6+6fW1zSqQtxRhsBAACUmEyCo/XmD7k1z5+R1GpmZyV9UdIZSYupvkVPSDogaY+kBjP7+c0UaGafM7PTZnZ6bGxsM6eiBAxEouoK1GtvS13G54SCAb18aVyzC0t5rAwAKlc2U8pCwYCck14aWr/P0dlLE5pdSNIYGwAAoMRkEhyNSupc9bxD7043kyQ556acc59xzh3Wco+jdknDkn5S0rBzbsw5tyDpu5JCqdOum9luSUr9vLHeizvnvuGcO+KcO9Le3p75O0PRW1xK6sRQPOPRRml9PW2aX0zq5YvjeaoMACrbQCSmth3VumfXjozPebCjRfXVXoVvM10tHInJY9LxboIjAACAUpJJcHRK0t1mdsDMqrXc3Pr51QeYWUtqnyQ9JemHzrkpLU9Re9TM6m258/Fjkt5KHfe8pE+nHn9a0p9u7a2g1Lx+eVI35xY3/e3z0QN+eT122w8nAIDsOecUjkTVG2zLaNGCtGqfR8cO+G87lTgcierQ3mY111XlqlQAAABsgw2DI+fcoqQvSPqelkOfP3LOnTOzz5vZ51OH3S/pnJmd1/Lqa19KnXtC0nckvSLp9dTrfSN1zjOSPmJm70j6SOo5Kkj6w0XvJr993lHj00MdzfQ5AoA8iIxN6/rUXFZTykLBgAZv3NKNqdn3bE/ML+rMpQn6GwEAAJQgXyYHOedekPTCmm1fX/V4QNLdtzn31yX9+jrbY1oegYQKNRCJ6b67GhXYsfnVdfp62vRvfxDRzdkFNdby7TUA5MpAajRndsHRcjAUjsT08YffXUfj1IVxLSYd/Y0AAABKUCZT1YCcm13Y2uo6vcGAlpJOJ4fXb8IKAMhOOBLT3pY67fPXb/rcg7ub1FxX9b6pxOHBqKq8pqNd/lyVCQAAgG1CcISCOHNpQnOL2a+u88i+VtX4PExXA4AcSiadBoZiCgUDm+pvlObxmHq7A+/7b3M4EtPD+1pVV+3NVakAAADYJgRHKIiBSFRej+l4d3bfPtdWeXWkq1X9gzTIBoBcefPqlCYSCwr1ZD+lLNQT0Oj4jC7FEpKkycSC3rgyyTQ1AACAEkVwhILoj8T0wN7mLfUnCgXbdP7aTcVuzeWwMgCoXAOpkUJbaWL9bp+j5WB/YCgm55Z70wEAAKD0EBxh292aW9SrIxNb/va5N3X+wBDT1QAgF/ojUQXbG7SrqTbrawTbG7SzsWZlutpAJKq6Kq8e6mjJUZUAAADYTgRH2HanLsS1mHRb/vb5wb3N2lHjo88RAOTAwlJSJ4ezX7QgzcwUCi73OXLOKRyJ6egBv6p9/MoBAABQivgtDtsuPBhVtc+jD+5v3dJ1fF6Pjh/wr0ytAABk77XRCSXml9S3hf5GaaGeNkVvzal/MKZ3btxSH/2NAAAAShbBEbZdOBLTB/e1qrZq66vr9AYDGo5O68rETA4qA4DK1T8Yk5l0/EAOgqNUUPTb33879Zz+RgAAAKWK4Ajbanx6Xm9encrZ6jrp6W5MVwOArQlHojq4u0mtDdVbvlZHa732+et15tKEmmp9OrinKQcVAgAAoBAIjrCtXkqtrrOVpZ5Xu3dXo/wN1Sur9wAANm92YUmvXJzI6cpn6SlvvcGAvB7L2XUBAACwvXyFLgCVJRyJqaHaqwdztLqOx2Pq7Q7oP792Va+PTubkmsXiJ+7fqV/76P2FLqPi/KdXr+jf/Nd35NzGx3rM9Gsfu08fvndnzl7/n//Zm7rvrkb93JHOnF3z3/5gUM+9cjln10P5mV9Kan4pubJaZS70Btv0rZMjTFMDAAAocQRH2Fb9kaiOHvCrypu7wW5P/fgBySSXySf9EjF445a+Gb6gf/iRe1Tj23ovKGTu3710UfHpBR07sHHz9r95J6r/cGokZ8HR5MyC/qB/WPfd1ZSz4Mg5p2f/5oIaa326f3djTq6J8hQKtqm3O3fB0U/ev1N/r++AfuahPTm7JgAAALYfwRG2zbXJWQ2NTeuTR/fl9LoP72vVV//Hra3QVmz+8s3reuoPT+vMpQk9msMPcrizmfklnbk0oc/0denXPrbxaK9//Mev6i/fuq5k0smTg6k4J4fjSjrpzatTGp+ez0mvmXdu3FL01px+9fF79XdzOIoJ2Eh9tU//+397sNBlAAAAYIvocYRtMzC03IcoV/2Nytmxbr88RtPv7fbyxfFNTdcJBQOaSCzorWtTOXn91b26XhrKzd99eDB137EcOgAAAIAsEBxh2/QPxtRSX6X772J1nY001VbpgY6WlQ/92B79kah8HtPRLn9Gx6d7t4QHcxXyxHT8gF8N1V7156jhe38kpn3+enW01ufkegAAAAAqC8ERtoVzTgORmHq7AzmZ0lMJ+oIBnR2Z0PTcYqFLqRjhSEwP72tRQ01ms3jvaq5Vd3tDTlb1G7s5p7ev39Tfurddxw74czLabCnp9NJQbGV1KwAAAADYLIIjbItL8YQuT8wolMOlnstdKNimxaTTqQvxQpdSESZnFvT66IR6N7kCVCgY0MnhuBaWklt6/fTUtFCwTaFgm4bGpnVtcnZL1zx3ZVI3Zxc3/Z4AAAAAII3gCNuifzD9oZiRD5n64P5WVXs99DnaJunG1Jv9NxoKtml6fkmvjU5s6fXDkagaa306tKdppcfSVkcype+7XK6UBQAAAKCyEBxhW4QjUe1qqlF3W0OhSykZddVePbK/JSfToLCxcCSq2iqPHt7Xsqnz0qHMVvschSMxHT8QkM/r0cHdTWqpr9pyaBiORHXvrka1N9Zs6ToAAAAAKhfBEfIu3d+oL9gmM/obbUYo2KZzV6Y0kZgvdCllLzwY09Euv2p83k2d19pQrYO7m7YU8oyOJ3QxllgZ7eTxmHq7AxqIxOScy+qac4tLOnUhnvEKcQAAAACwHoIj5N3b128qNj3PB9gshIIBOZe7pdmxvnRj6mz/jYaCAb18aVyzC0tZnZ8OnfpW9QALBQO6PDGji7FEVtc8e2lCswtJpocCAAAA2BKCI+RdegoPwdHmPdTZovpqL32O8iwdzPVl2US6r6dN84tJvXxxPKvzByIxBRqqdc+uHSvb0o3ks/27D0di8ph0nP5GAAAAALaA4Ah5F47EtD9Qr47W+kKXUnKqvJ6cLc2O20s3pv7Anqaszj96wC+vx7LqR+WcU/9gVL3BwHumcna3NWhXU03WPa7Ckage2Nus5rqqrM4HAAAAAIngCHm2uJTUiaGYQiwHnrVQMKDBG7d0fWprS7Pj9lY3ps7GjhqfHupoXlnFbDMiY9O6cXPuPdPUJMnMFAq2aSASUzK5uT5HiflFnbk0oV7uOwAAAABbRHCEvHrjypRuzi3SZ2UL0qHbAKOO8iLdmLqvZ2v/Rvt62vTa6ISmZhc2dd5AakTRevdIKBhQbHpeP7pxc1PXPHVhXItJt+X3BAAAAAAER8ir9DQb+htl7+DuJjXXVWU9ZQl3lp4GuNVRcb3BgJJOOjUc39R5/YMx7W2p0z7/+6dypu+b8CZHMoUHo6rymo7s92/qPAAAAABYi+AIeRUejOm+uxrVtqOm0KWUrPTS7P2D2S/NjtsbiMTUtuO9jamz8ci+VtX4PJuarpZMOg0MxRRa098oraO1XvsD9ZsODcORmB7e16q6au+mzgMAAACAtQiOkDdzi0s6dSHOaKMc6OtZXpp9JD5T6FLKyruNqdvWDW42o7bKqyNdrZsKed68OqXJmQWF7jClLBRs04mhuBaXkhldczKxoDeuTGa9QhwAAAAArJZRcGRmj5vZ22Y2aGZPr7O/1cyeM7PXzOykmR1Kbb/XzM6u+jNlZr+c2vcbZnZ51b6P5fSdoeDOXJrQ3GKSxtg5kG5yzHS13Eo3ps5VD65QsE3nr91U7NZcRseHV/ob3f4eCQUDujm3qDeuTGV0zYGhmJzTHcMoAAAAAMjUhsGRmXklfVXSRyUdlPRJMzu45rAvSzrrnHtQ0qckfUWSnHNvO+cOO+cOS/qgpISk51ad9zvp/c65F7b8blBUwoNReUw63k2fla0KtjdoZ2ON+mmQnVN3akydjfTouoGhzP6ewpGYgu0N2tVUu+E1+wczCw0HIlHVVXn1UEdLRscDAAAAwJ1kMuLomKRB59yQc25e0rclPbHmmIOSXpQk59x5SV1mtmvNMY9JijjnLm6xZpSIcCSmBzpa1FRbVehSSp6Zqa+nTQORKH2OcuhOjamz8eDeZu2o8a003L6ThaWkTg7HNxyR17ajRvfd1ZjxqnrhSEzHDvhV7WMmMgAAAICty+STxV5JI6uej6a2rfaqpE9Ikpkdk7RfUseaY56U9K01276Qmt72rJm1rvfiZvY5MzttZqfHxsYyKBfFYHpuUWdHJnI2kgPLI0+it+b1zo1bhS6lLGzUmDobPq9Hxw/4Mwp5Xh2ZUGJ+SX0ZTCnrDQZ06kJcc4tLdzzuxtSs3rlxi/sOAAAAQM5kEhyt94lq7ZCHZyS1mtlZSV+UdEbS4soFzKol/YykP151ztckBSUdlnRV0m+v9+LOuW8454445460t7dnUC6KwckLcS0mHQ16cyi0ySlLuLNMGlNnozcY0HB0Wlcm7tzIPByJyUw6fmDj1w8F2zS3mNSZSxN3PC49RY6+YgAAAAByJZPgaFRS56rnHZKurD7AOTflnPtMqpfRpyS1SxpedchHJb3inLu+6pzrzrkl51xS0u9qeUocysRAJKZqr0cf3L/uQDJk4d2l2elzlAuZNKbORl9PupH5nf+ewpGoDu5uUmtD9YbXPN7tl8eW+4bd8ZqDMTXXVengnqbMCwYAAACAO8gkODol6W4zO5AaOfSkpOdXH2BmLal9kvSUpB8651YvAfRJrZmmZma7Vz39WUlvbLZ4FK9wJKqH97Wortpb6FLKSigY0EtDMS0l6XO0VZk0ps7Gvbsa5W+ovuMKeDPzS3rl4sRKyLSRptoqPdDRsmEY1R+J6tFuv7ye3Ey9AwAAAIANgyPn3KKkL0j6nqS3JP2Rc+6cmX3ezD6fOux+SefM7LyWRxd9KX2+mdVL+oik76659G+a2etm9pqkvy3pV7b8blAUJhLzOndlKuMPxchcb7BNN2cX9cblyUKXUtIybUydDY/H1Nsd0EAkdttG5i9fHNf8UnJlxbRMhIIBnR2Z0PTc4rr7R+IJjY7PME0NAAAAQE75MjnIOfeCpBfWbPv6qscDku6+zbkJSe/7dOSc+4VNVYqS8dJQTM7lbolzvKu3e/l/03Akpoc6WwpbTAnbTGPqbIR6AvrPr1/VcHRa3e073rc/HInK5zEd6/JnfM2+YJu+9oOITl6I62/fu3Pda0rK23sCAAAAUJlYrxk5F47EVF/t1YMdLYUupey0N9bo3l2Nd5wGhY1tpjF1NtKjfm43taw/EtPhzhY11GSU3UuSPri/VdVez21XbOsfjKm9sUbBdYIqAAAAAMgWwRFyrn8wqmMH/Kr28c8rHzJdmh231z+YeWPqbHQF6rW7uXbdkGdqdkGvj05sekReXbVXD+9rWTc0dM4pHIkpFAzIjP5GAAAAAHKHT/bIqetTs4qMTTNNLY/6eto0u5DU2Q2WZsf6ZuaXdOZS5o2ps2FmCgXbFI5ElVzTyPzEUFxJt9yvarP6etp07sqUJhLz79n+zo1bit6aUx/9jQAAAADkGMERcio9woIGvflz7EBqafYNVtjC+rJpTJ2NUDCg8cSCzl+7+Z7t4UhUNT6PHtnfktU1nVvuI/aeaw4uj0LK93sCAAAAUHkIjpBT/YNRNddV6eDupkKXUraa66r0wN5m+hxlqT+LxtTZCPWkG5m/9+9pIBLT0S6/anzeTV/zwY4W1Vd73xcahiMxdfrr1Omvz75gAAAAAFgHwRFyJt1npbc7II+HPiv5FOpp05lLE0rMr780O24vnEVj6mzsbq5Td1vDe0Ke6K05nb92M+uRQdU+j44d8Kt/8N0wainp9NJQjGlqAAAAAPKC4Ag5MxKf0eWJmZWRFsifUDCgxaTTyeF4oUspKdk2ps5WbzCgE0MxLSwlJb07lXMr/ZVCwYAiY9O6PjUrSTp3ZVJTs4tMUwMAAACQFwRHyJn+1JQcGmPn35H9/jsuzY71baUxdTZCwTZNzy/p9cuTkpZHOzXW+HRoT/ZTOdP9w9J/9+kRTQRHAAAAAPKB4Ag5E47EtLOxRsH2HYUupey9uzQ7wdFmbKUxdTbSYU66eXU4EtXx7oB83uz/03twd5Oa66pWpqv1D0Z1z64d2tlYu/WCAQAAAGANgiPkhHNOA5GoQsGAzOhvtB1CwTa9cWXyfUuz4/a20pg6G/6Gat2/u0nhSEyj4wldjCW2PCLP4zH1dgcUjsQ0v5jUqQtxVjEEAAAAkDcER8iJH12/peiteT7AbqNQT3ppdvocZWKrjamzFQoGdPriuH7w9tjy8xz0AAv1BHR5YkbPv3pFswtJpqkBAAAAyBuCI+REeslxGmNvn4dSS7MPrFnuHevLRWPqbPT1BDS/mNTX/yqiQEO17t3VuOVrpgPaf/2XP5LHpEe7ue8AAAAA5AfBEXIiHIlpn79eHa31hS6lYlT7PDra5Vc/fY4yEo5Et9yYOhtHu/zyekyj4zPqzdFUzmB7g3Y21mh0fEaH9jarua4qB5UCAAAAwPv5Cl0ASt/iUlIvDcX0dx7YXehSKk4oGNC/+C/ndSE6rbbGmg2Pr/V5ttSYeT0z80taci6n18yHcCSm493+nL//jTTWVunBjmaduTSRs6mcZqZQMKA/OXuFaWoAAAAA8orgCFt27sqUbs4u8gG2ANLTrj78Wz/I6Phge4Ne/Ecfztnrf+/cNf3i//Nyzq6Xb5/u7SrI6/5YT5vOXJpQXw6ncvb1tOlPzl5RH33FAAAAAOQRwRG2LL0kPI2xt98H9jTpt3/uIcWnN15Z7eSFuL7/5nVNzizkbGrT2ZEJ+Tymf/r4fTm5Xj5VeU3//ZHOgrz2Uz/erYc6WrQ/0JCzaz5xeK/qqr36sW3u2QQAAACgshAcYcvCkaju2bVD7RlMlUJumZn+uw92ZHTs3tY6ff/N6xqJJ9S8tzknrz8ST2hva53+/oe6c3K9ctVcV6WfPLgrp9es9nn00w/uyek1AQAAAGAtmmNjS+YWl3TqQpzRRiWgM9W4fHQ8kbNrjozPrFwXAAAAAFB+CI6wJWcvTWh2IakQ/Y2KXqe/TpI0Ep/J2TVH44mV6wIAAAAAyg/BEbakPxKTx6Tj3QRHxa65rkqNNT6N5GjE0fTcomLT8+pgxBEAAAAAlC2CI2zJQCSqB/Y256zZMvLHzNTpr9dIPDfB0ej48silfX6CIwAAAAAoVwRHyFpiflFnLk2ol/5GJaPTX6dLOQqO0tfpJDgCAAAAgLJFcISsnRyOazHp6G9UQjpb6zU6PiPn3JavlR651NlKjyMAAAAAKFcER8jaQCSmKq/paJe/0KUgQ53+es0tJjV2c27L1xoZT6i+2it/Q3UOKgMAAAAAFCOCI2QtHInp4X2tqqv2FroUZGhlZbUcNMgeic+os7VeZrblawEAAAAAihPBEbIykZjXG1cmmaZWYjpTK6CNxGe2fK3R8cRKEAUAAAAAKE8ER8jKS0NxOSf19dAYu5R0rARHWxtx5JzTSDxBY2wAAAAAKHMZBUdm9riZvW1mg2b29Dr7W83sOTN7zcxOmtmh1PZ7zezsqj9TZvbLqX1+M/u+mb2T+tma03eGvBqIRFVX5dVDHS2FLgWbUFftVXtjzZZXVotPz2t6fmllBBMAAAAAoDxtGByZmVfSVyV9VNJBSZ80s4NrDvuypLPOuQclfUrSVyTJOfe2c+6wc+6wpA9KSkh6LnXO05JedM7dLenF1HOUiP5ITMcO+FXtY9Baqelsrdtyj6OR8eWpbow4AgAAAIDylsmn/mOSBp1zQ865eUnflvTEmmMOajn8kXPuvKQuM9u15pjHJEWccxdTz5+Q9M3U429K+vjmy0ch3Jia1eCNW/Q3KlGd/vot9zhKT3WjxxEAAAAAlLdMgqO9kkZWPR9NbVvtVUmfkCQzOyZpv6SONcc8Kelbq57vcs5dlaTUz52Zl41CGhiKSZJCQfoblaLO1npdnZzRwlIy62ukRywxVQ0AAAAAylsmwdF6a227Nc+fkdRqZmclfVHSGUmLKxcwq5b0M5L+eLMFmtnnzOy0mZ0eGxvb7OnIg/7BqJrrqnRwT1OhS0EW9vnrlXTS1YnZrK8xEp9RoKFaDTW+HFYGAAAAACg2mQRHo5I6Vz3vkHRl9QHOuSnn3GdSvYw+Jald0vCqQz4q6RXn3PVV266b2W5JSv28sd6LO+e+4Zw74pw70t7enkG5yLdwJKZHu/3yetbLFFHsOlLTy7bS52h0PKEO+hsBAAAAQNnLJDg6JeluMzuQGjn0pKTnVx9gZi2pfZL0lKQfOuemVh3ySb13mppS1/h06vGnJf3pZovH9huJJzQ6PsM0tRKWnl62lZXVLsUT6mylvxEAAAAAlLsNgyPn3KKkL0j6nqS3JP2Rc+6cmX3ezD6fOux+SefM7LyWRxd9KX2+mdVL+oik76659DOSPmJm76T2P7PVN4P86x+MSpL6emiMXap2N9fK67GVBtebtZR0ujIxw4pqAAAAAFABMmpQ4px7QdILa7Z9fdXjAUl33+bchKT3pQzOuZiWV1pDCQlHYmpvrFGwfUehS0GWfF6P9rTUamQ8u5XVrk3NamHJ0RgbAAAAACpAJlPVAEmSc07hSEyhYEBm9DcqZZ2t9VmPOEqf1+lnqhoAAAAAlDuCI2Rs8MYtRW/NqY/+RiVvn79eo1k2x04HR/uYqgYAAAAAZY/gCBlL9zfqDdLfqNR1+usVvTWv6bnFTZ87Ek/IY9KeFkYcAQAAAEC5IzhCxsKRmDr9dTRFLgMdqRXRRrPoczQyPqPdzXWq8vKfDwAAAAAod3zyQ0aWkk4vDcWYplYm0uFfNn2ORuKJleAJAAAAAFDeCI6QkXNXJjU1u8g0tTKRXhFtJIs+RyPjCUadAQAAAECFIDhCRsKRmCT6G5WLth3VqqvyaiS+ualqswtLuj41txI8AQAAAADKG8ERMhKOxHTPrh3a2Vhb6FKQA2amTn/dpkccXZ5YDpr2BZiqBgAAAACVgOAIG5pfTOrUcFwh+huVlc7W+k33OLqUOp4RRwAAAABQGQiOsKGzIxOaWVhimlqZ6fQvB0fOuYzPGU0HR/Q4AgAAAICKQHCEDYUjUXlMerSb4KicdLTWaXp+SeOJhYzPGRmfUbXPo/YdNXmsDAAAAABQLAiOsKHwYEyH9jarua6q0KUgh9KjhjYzXW0knlBHa508HstXWQAAAACAIkJwhDtKzC/qzMg409TKULpP0WYaZI+MJ+hvBAAAAAAVhOAId3T6wrgWlpz6aIxddjr9yyujjcRnMj5nJD6jffQ3AgAAAICKQXCEO+qPRFXlNR3pai10KcixxtoqtdZXrayUtpHJmQVNziysBE4AAAAAgPJHcIQ7GojE9PC+VtVX+wpdCvKg01+v0QynqqV7ITFVDQAAAAAqB8ERbmsysaA3Lk8qRH+jstXZWp9xc+x0wNTJVDUAAAAAqBgER7itl4ZjSjopRH+jstXhr9PliRktJd2Gx6Z7ITHiCAAAAAAqB/OPysSF6LRGxzNvcpyJ51+9oroqrw53tuT0uige+/z1Wlhyuj41qz0td+5dNDKeUFOtT831VdtUHQAAAACg0AiOysDiUlJPfLVfkzMLOb/2Y/ftVLWPgWnlKj16aCSe2Dg4iieYpgYAAAAAFYbgqAy8cWVKkzML+ic/da+OHfDn9Nr37GrM6fVQXNJB0KV4Qse779zL6lI8obt38u8BAAAAACoJwVEZODkckyT93JEO7WysLXA1KCV7WmplJo1sMM3ROafR8Rn9xH07t6kyAAAAAEAxYA5SGTgxFFd3WwOhETatxufVXU21Gt1gZbWxm3OaW0wyVQ0AAAAAKgzBUYlbSjqdvBDX8e7cTlFD5ehsrdfI+J2Do/R+VlQDAAAAgMpCcFTizl+b0s3ZxZz3NkLl6PTXayR+56lq6f2MOAIAAACAykJwVOJODMUlSccP3LmxMXA7nf46Xb85q9mFpdseM5KaytbReueV1wAAAAAA5YXgqMSdGI6p01+34VLqwO10ttbLOenyxO1HHV2KJ7SzsUa1Vd5trAwAAAAAUGgERyXMOaeTw3Ed62K0EbKXnn42cocG2SPjCaapAQAAAEAFyig4MrPHzextMxs0s6fX2d9qZs+Z2WtmdtLMDq3a12Jm3zGz82b2lpn1prb/hpldNrOzqT8fy93bqgzv3Lil8cQCjbGxJZ3+5dFqI+O3H3E0Ep9RJ9PUAAAAAKDibBgcmZlX0lclfVTSQUmfNLODaw77sqSzzrkHJX1K0ldW7fuKpD93zt0n6SFJb63a9zvOucOpPy9s4X1UpBNDMUnSo/Q3whbsaqxVtdej0duMOFpYSurq5AwjjgAAAACgAmUy4uiYpEHn3JBzbl7StyU9seaYg5JelCTn3HlJXWa2y8yaJH1I0u+n9s075yZyVXylOzEc111NtSsjRoBseDymjtY6jYyvHxxdnZhV0rGiGgAAAABUokyCo72SRlY9H01tW+1VSZ+QJDM7Jmm/pA5J3ZLGJP2BmZ0xs98zs4ZV530hNb3tWTNrzfZNVCLnnE4Mx3W82y8zK3Q5KHEd/nqNxNefqpYOlDpbCY4AAAAAoNJkEhytl0q4Nc+fkdRqZmclfVHSGUmLknySHpH0Nefcw5KmJaV7JH1NUlDSYUlXJf32ui9u9jkzO21mp8fGxjIotzIMR6c1dnNOx5mmhhzobK3TpdtMVUtvZ2QbAAAAAFSeTIKjUUmdq553SLqy+gDn3JRz7jPOucNa7nHULmk4de6oc+5E6tDvaDlIknPuunNuyTmXlPS7Wp4S9z7OuW8454445460t7dn/s7K3MnhuCTp2AEaY2PrOv31mpxZ0NTswvv2jcQT8nlMu5sJjgAAAACg0mQSHJ2SdLeZHTCzaklPSnp+9QGpldOqU0+fkvTDVJh0TdKImd2b2veYpDdT5+xedYmflfTGFt5HxTkxHFfbjhoF2xs2PhjYQHoa2sg6o45Gxme0p6VOXg9TIgEAAACg0vg2OsA5t2hmX5D0PUleSc86586Z2edT+78u6X5Jf2hmS1oOhj676hJflPTvU8HSkKTPpLb/ppkd1vK0twuSfjEn76hCnByO6/gB+hshN9LT0EbiM/rAnub37BuJJ5imBgAAAAAVasPgSJKccy9IemHNtq+vejwg6e7bnHtW0pF1tv/CZgrFu0biCV2emNHnPtRd6FJQJvalVkwbXWdltdHxhD5ycNd2lwQAAAAAKAKZTFVDkTmR6m90vJv+RsiN5roqNdb43jdVLTG/qOiteXWwohoAAAAAVCSCoxJ0cjimlvoq3bOzsdCloEyYmTr89e9bWW0kPiNpuXk2AAAAAKDyEByVoBPDcR3t8stDs2LkUGdrnUbGZ96zLT0CqbOVHkcAAAAAUIkIjkrMtclZXYwldPwA09SQW53+eo2OJ+ScW9k2kup5xIgjAAAAAKhMBEcl5sRwTJL0aHegwJWg3HS21ml2IamxW3Mr20biM6qr8irQUF3AygAAAAAAhUJwVGJODMfVWOPT/bubCl0Kysy+wPKoonRfI2l5xNE+f73MmBYJAAAAAJWI4KjEnBiK6UhXq7z0N0KOdbamg6N3G2SPxBPq9NPfCAAAAAAqFcFRCYnemlNkbFrHmaaGPOhYExw55zQST6xsBwAAAABUHoKjEnJyOC5JOkZjbORBXbVXbTtqVhpijycWND2/RGNsAAAAAKhgBEcl5MRQTPXVXj2wt7nQpaBMdfrrVnocpUcedbYyVQ0AAAAAKhXBUQk5MRzXB/e3qsrLXxvyY5+/fmXEUfpnumk2AAAAAKDykECUiInEvN6+flPHupimhvzpbK3X1clZLSwlV0YeddLjCAAAAAAqFsFRiTg5HJdzojE28qrTX6elpNPViVldiifkb6hWQ42v0GUBAAAAAAqE4KhEnByOq9rn0UOd9DdC/qRHF42MJzQ6nqC/EQAAAABUOIKjEnFiOK6HO1tU4/MWuhSUsfQKaiPxhEbiCXWwohoAAAAAVDSCoxIwNbugc1cmmaaGvNvdXCuvx3QxntDliRn6GwEAAABAhSM4KgEvXxxX0knHD9AYG/nl83q0p6VWL18Y18KS0z5GHAEAAABARSM4KgEnhuKq8poe2dda6FJQATpb63VmZHz5sZ8eRwAAAABQyQiOSsDJ4Zge7GhRXTX9jZB/na31WlhyK48BAAAAAJWL4KjIJeYX9dropI4xTQ3bJD3KyEza08KIIwAAAACoZARHRe6VixNaTDr6G2HbpFdW291Uq2of/4kAAAAAgErGp8Iid3I4Jo9JR7oIjrA9OlLT0zpojA0AAAAAFY/gqMi9NBzXob3N2lHjK3QpqBDpldRYUQ0AAAAAQHBUxGYXlnR2ZIJpathWbTuqdWhvkx7tDhS6FAAAAABAgTGMpYi9OjKh+cWkjh/gAzy2j5npz77444UuAwAAAABQBBhxVMRODMdlJh2lvxEAAAAAACgAgqMiFo5E9YE9TWquryp0KQAAAAAAoAIRHBWpmfklvXJxQqFgW6FLAQAAAAAAFSqj4MjMHjezt81s0MyeXmd/q5k9Z2avmdlJMzu0al+LmX3HzM6b2Vtm1pva7jez75vZO6mfrbl7W6Xv5Yvjml9KqjdIfyMAAAAAAFAYGwZHZuaV9FVJH5V0UNInzezgmsO+LOmsc+5BSZ+S9JVV+74i6c+dc/dJekjSW6ntT0t60Tl3t6QXU8+REo5E5fOYjtHfCAAAAAAAFEgmI46OSRp0zg055+YlfVvSE2uOOajl8EfOufOSusxsl5k1SfqQpN9P7Zt3zk2kznlC0jdTj78p6eNbeB9lpz8S0+HOFjXUsPAdAAAAAAAojEyCo72SRlY9H01tW+1VSZ+QJDM7Jmm/pA5J3ZLGJP2BmZ0xs98zs4bUObucc1clKfVz53ovbmafM7PTZnZ6bGwsw7dV2qZmF/T66IRCTFMDAAAAAAAFlElwZOtsc2uePyOp1czOSvqipDOSFiX5JD0i6WvOuYclTWuTU9Kcc99wzh1xzh1pb2/fzKkl6+RQXEknhXpojA0AAAAAAAonk3lQo5I6Vz3vkHRl9QHOuSlJn5EkMzNJw6k/9ZJGnXMnUod+R+8GR9fNbLdz7qqZ7ZZ0I+t3UWb6I1HV+Dx6eF9LoUsBAAAAAAAVLJMRR6ck3W1mB8ysWtKTkp5ffUBq5bTq1NOnJP3QOTflnLsmacTM7k3te0zSm6nHz0v6dOrxpyX96RbeR1kZiMR0tMuvGp+30KUAAAAAAIAKtuGII+fcopl9QdL3JHklPeucO2dmn0/t/7qk+yX9oZktaTkY+uyqS3xR0r9PBUtDSo1M0vL0tj8ys89KuiTp53L0nkpa9Naczl+7qV99fE+hSwEAAAAAABUuoyW7nHMvSHphzbavr3o8IOnu25x7VtKRdbbHtDwCCasMRGKSpFCQ/kYAAAAAAKCwMpmqhm0UjsTUWOPToT1NhS4FAAAAAABUOIKjIhOORHW8OyCfl78aAAAAAABQWKQTRWR0PKGLsYRCwUChSwEAAAAAACA4KiYr/Y16CI4AAAAAAEDhERwVkXAkpkBDte7d1VjoUgAAAAAAAAiOioVzTuFIVL3BgMys0OUAAAAAAAAQHBWLoei0rk/NKRRsK3QpAAAAAAAAkgiOikZ4MCpJ6qO/EQAAAAAAKBIER0UiHIlpb0ud9vnrC10KAAAAAACAJIKjopBMOg0MxehvBAAAAAAAigrBURF48+qUJhILTFMDAAAAAABFheCoCAxEYpKk3m4aYwMAAAAAgOJBcFQEwpGoutsbdFdzbaFLAQAAAAAAWEFwVGALS0mdHI6rL8hoIwAAAAAAUFwIjgrstdEJTc8vKRSkvxEAAAAAACguBEcF1j8Yk5n0aDfBEQAAAAAAKC4ERwUWjkR1cHeTWhuqC10KAAAAAADAexAcFdDswpJeuTjBNDUAAAAAAFCUCI4K6PSFcc0vJRXqoTE2AAAAAAAoPgRHBRSOROXzmI52+QtdCgAAAAAAwPsQHBVQOBLTQ50t2lHjK3QpAAAAAAAA70NwVCBTswt6bXRCffQ3AgAAAAAARYrgqEBODsWVdFJvkP5GAAAAAACgOBEcFUg4ElONz6OH97UUuhQAAAAAAIB1ERwVSDgS1dEuv2qrvIUuBQAAAAAAYF0ERwUQvTWn89duqpf+RgAAAAAAoIgRHBXAS0MxSVKI4AgAAAAAABSxjIIjM3vczN42s0Eze3qd/a1m9pyZvWZmJ83s0Kp9F8zsdTM7a2anV23/DTO7nNp+1sw+lpu3VPz6B2NqrPHpgb3NhS4FAAAAAADgtnwbHWBmXklflfQRSaOSTpnZ8865N1cd9mVJZ51zP2tm96WOf2zV/r/tnIuuc/nfcc79Vvbll6aBSFTHu/3yeRnwBQAAAAAAilcmycUxSYPOuSHn3Lykb0t6Ys0xByW9KEnOufOSusxsV04rLROXJ2Z0IZZQb7Ct0KUAAAAAAADcUSbB0V5JI6uej6a2rfaqpE9Ikpkdk7RfUkdqn5P0F2b2spl9bs15X0hNb3vWzFo3XX0J2tNcq+/98of0Mw/tKXQpAAAAAAAAd5RJcGTrbHNrnj8jqdXMzkr6oqQzkhZT+/qcc49I+qikXzKzD6W2f01SUNJhSVcl/fa6L272OTM7bWanx8bGMii3uJmZ7r2rUe2NNYUuBQAAAAAA4I4yCY5GJXWuet4h6crqA5xzU865zzjnDkv6lKR2ScOpfVdSP29Iek7LU9/knLvunFtyziUl/W56+1rOuW8454445460t7dv5r0BAAAAAABgCzIJjk5JutvMDphZtaQnJT2/+gAza0ntk6SnJP3QOTdlZg1m1pg6pkHSfyPpjdTz3asu8bPp7QAAAAAAACgOG66q5pxbNLMvSPqeJK+kZ51z58zs86n9X5d0v6Q/NLMlSW9K+mzq9F2SnjOz9Gv9v865P0/t+00zO6zlaW8XJP1irt4UAAAAAAAAts6cW9uuqHgdOXLEnT59utBlAAAAAAAAlA0ze9k5d2S9fZlMVQMAAAAAAEAFIjgCAAAAAADAugiOAAAAAAAAsC6CIwAAAAAAAKyL4AgAAAAAAADrIjgCAAAAAADAusw5V+gaMmZmY5Iu5uhybZKiOboWUMm4l4Dc4X4CcoN7CcgN7iUgd4r9ftrvnGtfb0dJBUe5ZGannXNHCl0HUOq4l4Dc4X4CcoN7CcgN7iUgd0r5fmKqGgAAAAAAANZFcAQAAAAAAIB1VXJw9I1CFwCUCe4lIHe4n4Dc4F4CcoN7Ccidkr2fKrbHEQAAAAAAAO6skkccAQAAAAAA4A4qLjgys8fN7G0zGzSzpwtdD1BKzKzTzP4/M3vLzM6Z2ZdS2/1m9n0zeyf1s7XQtQKlwMy8ZnbGzP4s9Zx7CdgkM2sxs++Y2fnU/z/1ci8B2TGzX0n9jveGmX3LzGq5n4CNmdmzZnbDzN5Yte22946Z/Voqk3jbzH6qMFVnrqKCIzPzSvqqpI9KOijpk2Z2sLBVASVlUdI/cs7dL+lRSb+UuoeelvSic+5uSS+mngPY2JckvbXqOfcSsHlfkfTnzrn7JD2k5XuKewnYJDPbK+kfSDrinDskySvpSXE/AZn4vyU9vmbbuvdO6vPTk5I+kDrn36ayiqJVUcGRpGOSBp1zQ865eUnflvREgWsCSoZz7qpz7pXU45ta/uV8r5bvo2+mDvumpI8XpECghJhZh6S/I+n3Vm3mXgI2wcyaJH1I0u9LknNu3jk3Ie4lIFs+SXVm5pNUL+mKuJ+ADTnnfigpvmbz7e6dJyR92zk355wbljSo5ayiaFVacLRX0siq56OpbQA2ycy6JD0s6YSkXc65q9JyuCRpZwFLA0rFv5b0q5KSq7ZxLwGb0y1pTNIfpKZ9/p6ZNYh7Cdg059xlSb8l6ZKkq5ImnXN/Ie4nIFu3u3dKLpeotODI1tnGsnLAJpnZDkn/UdIvO+emCl0PUGrM7Kcl3XDOvVzoWoAS55P0iKSvOeceljQtptEAWUn1X3lC0gFJeyQ1mNnPF7YqoCyVXC5RacHRqKTOVc87tDz8EkCGzKxKy6HRv3fOfTe1+bqZ7U7t3y3pRqHqA0pEn6SfMbMLWp42/RNm9u/EvQRs1qikUefcidTz72g5SOJeAjbvJyUNO+fGnHMLkr4rKSTuJyBbt7t3Si6XqLTg6JSku83sgJlVa7kh1fMFrgkoGWZmWu4j8ZZz7l+t2vW8pE+nHn9a0p9ud21AKXHO/ZpzrsM516Xl/y/6r865nxf3ErApzrlrkkbM7N7UpsckvSnuJSAblyQ9amb1qd/5HtNyP0vuJyA7t7t3npf0pJnVmNkBSXdLOlmA+jJmzhX1iKicM7OPabmvhFfSs865/6OwFQGlw8x+TNJfS3pd7/Zl+bKW+xz9kaR9Wv6l4+ecc2ubwwFYh5l9WNI/ds79tJkFxL0EbIqZHdZyk/lqSUOSPqPlL0e5l4BNMrN/Jul/0PJKumckPSVph7ifgDsys29J+rCkNknXJf26pD/Rbe4dM/tfJP09Ld9rv+yc+y/bX3XmKi44AgAAAAAAQGYqbaoaAAAAAAAAMkRwBAAAAAAAgHURHAEAAAAAAGBdBEcAAAAAAABYF8ERAAAAAAAA1kVwBAAAAAAAgHURHAEAAAAAAGBdBEcAAAAAAABY1/8P14h9MqrxA48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(3,100),score_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888888888888889"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(score_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_list.index(max(score_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc =  RandomForestClassifier(n_estimators=45,n_jobs=-1,random_state=260)\n",
    "rfc = rfc.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DecisionTreeClassifier(max_features='auto', random_state=414944266),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=260415631),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=934496186),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=292741929),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1113832566),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1020971958),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1201788394),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=633405727),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=590467324),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=783001137),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=731814326),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1379473122),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1764069317),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1737411384),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1480588278),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=456667912),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=2085478848),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1895644608),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=115661196),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=482586323),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1188859346),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=929507193),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1000597533),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1527856282),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1668373917),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=106103646),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=642402532),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=652518842),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=424817230),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=779126451),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=823527030),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=190483317),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1543347613),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1482322122),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=650777511),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1293428245),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1974383709),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1380302569),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=320848823),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=628965023),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=438149175),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1603350653),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1847880810),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=1258009203),\n",
       " DecisionTreeClassifier(max_features='auto', random_state=200038776)]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.estimators_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260415631"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.estimators_[1].random_state #树的随机种子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用袋外数据进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc =  RandomForestClassifier(n_estimators=45,oob_score=True,random_state=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=45, oob_score=True, random_state=250)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(wine.data,wine.target)# 使用袋外数据评估时，不需要切分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9887640449438202"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.oob_score_ #使用袋外数据进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 0, 0, 1, 0, 1, 1, 2, 1, 0, 1, 1, 0, 1, 2, 2, 2, 1, 1, 2,\n",
       "       2, 0, 0, 1, 0, 2, 0, 0, 2, 0, 1, 0, 2, 0, 1, 1, 2, 0, 0, 0, 2, 1,\n",
       "       2, 2, 0, 0, 2, 1, 1, 1, 2, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        , 0.        ],\n",
       "       [0.06666667, 0.06666667, 0.86666667],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.08888889, 0.86666667, 0.04444444],\n",
       "       [0.95555556, 0.04444444, 0.        ],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [0.02222222, 0.97777778, 0.        ],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.04444444, 0.95555556, 0.        ],\n",
       "       [0.82222222, 0.15555556, 0.02222222],\n",
       "       [0.02222222, 0.97777778, 0.        ],\n",
       "       [0.06666667, 0.88888889, 0.04444444],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [0.        , 0.06666667, 0.93333333],\n",
       "       [0.        , 0.02222222, 0.97777778],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.        , 0.93333333, 0.06666667],\n",
       "       [0.        , 0.95555556, 0.04444444],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.04444444, 0.        , 0.95555556],\n",
       "       [0.95555556, 0.04444444, 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.        , 0.04444444, 0.95555556],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.02222222, 0.        , 0.97777778],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.02222222, 0.95555556, 0.02222222],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.97777778, 0.02222222, 0.        ],\n",
       "       [0.04444444, 0.95555556, 0.        ],\n",
       "       [0.02222222, 0.97777778, 0.        ],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.91111111, 0.08888889, 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.97777778, 0.02222222, 0.        ],\n",
       "       [0.04444444, 0.        , 0.95555556],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [0.        , 0.04444444, 0.95555556],\n",
       "       [0.        , 0.08888889, 0.91111111],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.97777778, 0.02222222, 0.        ],\n",
       "       [0.        , 0.        , 1.        ],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [0.13333333, 0.86666667, 0.        ],\n",
       "       [0.        , 1.        , 0.        ],\n",
       "       [0.02222222, 0.04444444, 0.93333333],\n",
       "       [1.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict_proba(Xtest) #预测概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  5,  5, ...,  3,  5,  2],\n",
       "       [20,  8,  7, ..., 13,  4,  9],\n",
       "       [ 6,  5, 16, ...,  3,  5,  2],\n",
       "       ...,\n",
       "       [ 6,  5,  5, ...,  3,  5,  2],\n",
       "       [14,  2,  7, ..., 13, 10,  7],\n",
       "       [24, 16, 24, ..., 19, 12, 12]], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.apply(Xtest) # 返回每个样本所处的叶子节点的索引值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adaboost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导包&构造数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_gaussian_quantiles\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1,y1 = make_gaussian_quantiles(cov=2.0,n_samples=500,n_classes=2,n_features=2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d115a91640>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[:,0],X1[:,1],c=y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2,y2 = make_gaussian_quantiles(mean=(3,3),cov=2.0,n_samples=500,n_classes=2,n_features=2,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d1184e0760>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[:,0],X1[:,1],c=y1)\n",
    "plt.scatter(X2[:,0],X2[:,1],c=y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将两组数据合成一组\n",
    "X = np.concatenate((X1,X2))\n",
    "y = np.concatenate((y1,-y2+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d1184a7f40>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1],c=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用Adaboost进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建基分类器\n",
    "clf = DecisionTreeClassifier(max_depth=2,\n",
    "                      min_samples_leaf=5, #叶子节点\n",
    "                      min_samples_split=20,#内部节点\n",
    "                      random_state=260\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建Adaboost\n",
    "ada = AdaBoostClassifier(base_estimator=clf,n_estimators=100,learning_rate=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自构建数据集切分\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.3,random_state = 260)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                         min_samples_leaf=5,\n",
       "                                                         min_samples_split=20,\n",
       "                                                         random_state=260),\n",
       "                   learning_rate=0.8, n_estimators=100)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8766666666666667"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 红酒数据集切分\n",
    "x_train,x_test,y_train,y_test = train_test_split(wine.data,wine.target,test_size=0.3,random_state = 260)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                         min_samples_leaf=5,\n",
       "                                                         min_samples_split=20,\n",
       "                                                         random_state=260),\n",
       "                   learning_rate=0.8, n_estimators=100)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ada.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 通过网格搜索调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "params_dict = {\n",
    "     'n_estimators':range(10,100,10),\n",
    "     'learning_rate':np.arange(0.1,1.1,0.1),\n",
    "     'algorithm':['SAMME.R','SAMME'],\n",
    "     'base_estimator__max_depth':range(2,8)#对基分类器参数进行优化\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 网格搜索\n",
    "grid = GridSearchCV(ada,params_dict,cv=5,n_jobs=-1,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 1080 candidates, totalling 5400 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    1.6s\n",
      "[Parallel(n_jobs=-1)]: Done 212 tasks      | elapsed:    4.8s\n",
      "[Parallel(n_jobs=-1)]: Done 618 tasks      | elapsed:   11.6s\n",
      "[Parallel(n_jobs=-1)]: Done 1184 tasks      | elapsed:   21.5s\n",
      "[Parallel(n_jobs=-1)]: Done 1914 tasks      | elapsed:   37.7s\n",
      "[Parallel(n_jobs=-1)]: Done 2804 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 3858 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 5072 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 5400 out of 5400 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=2,\n",
       "                                                                                min_samples_leaf=5,\n",
       "                                                                                min_samples_split=20,\n",
       "                                                                                random_state=260),\n",
       "                                          learning_rate=0.8, n_estimators=100),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'algorithm': ['SAMME.R', 'SAMME'],\n",
       "                         'base_estimator__max_depth': range(2, 8),\n",
       "                         'learning_rate': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       "                         'n_estimators': range(10, 100, 10)},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9242857142857144"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'SAMME.R',\n",
       " 'base_estimator__max_depth': 6,\n",
       " 'learning_rate': 0.2,\n",
       " 'n_estimators': 30}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.score(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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